Abstract
The Internet of Things (IoT) envisions a smart environment powered by connectivity and heterogeneity where ensuring reliable services and communications across multiple industries, from financial fields to healthcare and fault detection systems, is a top priority. In such fields, data is being collected and broadcast at high speed on a continuous and real-time scale, including IoT in the streaming processing paradigm. Intrusion Detection Systems (IDS) rely on manually defined security policies and signatures that fail to design a real-time solution or prevent zero-day attacks. Therefore, anomaly detection appears as a prominent solution capable of recognizing patterns, learning from experience, and detecting abnormal behavior. However, most approaches do not fit the urged requirements, often evaluated on deprecated datasets not representative of the working environment. As a result, our contributions address an overview of cybersecurity threats in IoT, important recommendations for a real-time IDS, and a real-time dataset setting to evaluate a security system covering multiple cyber threats. The dataset used to evaluate current host-based IDS approaches is publicly available and can be used as a benchmark by the community.

Abstract
A honeypot is a controlled and secure environment to examine different threats and understand attack patterns. Due to the highly dynamic environments, the growing adoption and use of Internet of Things (IoT) devices make configuring honeypots complex. One of the current literature challenges is the need for a honeypot not to be detected by attackers, namely due to the delays that are required to make requests to external and remote servers. This work focuses on deploying honeypots virtually on IOT devices. With this technology, we can use endpoints to send specific honeypots on recent known vulnerabilities on IOT devices to find and notify attacks within the network, as much of this information is verified and made freely available by government entities. Unlike other approaches, the idea is not to have a fixed honeypot but a set of devices that can be used at any time as a honeypot (adapted to the latest threat) to test the network for a possible problem and then report to Threat Sharing Platform (TSP). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Purpose The aim of this paper is to evaluate the use of blockchain for identity management (IdM) in the context of the Internet of things (IoT) while focusing on privacy-preserving approaches and its applications to healthcare scenarios. Design/methodology/approach The paper describes the most relevant IdM systems focusing on privacy preserving with or without blockchain and evaluates them against ten selected features grouped into three categories: privacy, usability and IoT. Then, it is important to analyze whether blockchain should be used in all scenarios, according to the importance of each feature for different use cases. Findings Based on analysis of existing systems, Sovrin is the IdM system that covers more features and is based on blockchain. For each of the evaluated use cases, Sovrin and UniquID were the chosen systems. Research limitations/implications This paper opens new lines of research for IdM systems in IoT, including challenges related to device identity definition, privacy preserving and new security mechanisms. Originality/value This paper contributes to the ongoing research in IdM systems for IoT. The adequacy of blockchain is not only analyzed considering the technology; instead the authors analyze its application to real environments considering the required features for each use case.

Abstract
The Internet of Things (IoT) is based on objects or things that have the ability to communicate and transfer data. Due to the large number of connected objects and devices, there has been a rapid growth in the amount of data that are transferred over the Internet. To support this increase, the heterogeneity of devices and their geographical distributions, there is a need for IoT gateways that can cope with this demand. The SOFTWAY4IoT project, which was funded by the National Education and Research Network (RNP), has developed a software-defined and virtualized IoT gateway that supports multiple wireless communication technologies and fog/cloud environment integration. In this work, we propose a planning method that uses optimization models for the deployment of IoT gateways in smart campuses. The presented models aimed to quantify the minimum number of IoT gateways that is necessary to cover the desired area and their positions and to distribute IoT devices to the respective gateways. For this purpose, the communication technology range and the data link consumption were defined as the parameters for the optimization models. Three models are presented, which use LoRa, Wi-Fi, and BLE communication technologies. The gateway deployment problem was solved in two steps: first, the gateways were quantified using a linear programming model; second, the gateway positions and the distribution of IoT devices were calculated using the classical K-means clustering algorithm and the metaheuristic particle swarm optimization. Case studies and experiments were conducted at the Samambaia Campus of the Federal University of Goias as an example. Finally, an analysis of the three models was performed, using metrics such as the silhouette coefficient. Non-parametric hypothesis tests were also applied to the performed experiments to verify that the proposed models did not produce results using the same population.

Abstract

Abstract
Physiological signals offer a vast amount of information about the well-being of the human system. Understanding the behavior and complexity of these signs is important for accurate assessments and diagnoses. This study focuses on fetal heart rate (FHR) analysis and its potential to detect perinatal asphyxia by analyzing how different representations of the FHR series could aid in asphyxia detection. Additionally, different compression schemes were applied to evaluate the potential of compression as a measure of complexity. For this purpose, text files containing data of the last hour of the FHR before birth were converted into different types of images (Time Series, Time Series with fixed axes, Recurrence Plot and Poincaré Plot). We then applied compression schemes for text (BZIP2 and GZIP) and images (Lempel-Ziv-Welch, DEFLATE, and JPG) in 5, 10, and 30-minute windows. Correlation analysis revealed that similar compressed formats, such as BZIP2/GZIP and TIFF LZW/TIFF DEFLATE/JPG LOSSY/JPG LOSSLESS, showed the highest values and the correlation between uncompressed and compressed formats became increasingly more negative for larger time windows. Mann-Whitney test between groups (with and without asphyxia) revealed that compressed patterned images, such as Recurrence Plots, showed the highest potential in detecting asphyxia. Moreover, we confirm that larger time windows allow for better detection, due to the presence of more detailed patterns. These findings confirmed the potential of time series image representation in detecting fetal conditions, as well as show that the compression of images leads to better results than the compression of text files. © 2022 IEEE.